PEA-PODs: Perceptual Evaluation of Algorithms for Power Optimization in XR Displays

🏆 Best Paper Award (Honorable Mention)

• Kenneth Chen, New York University & Meta


• Thomas Wan, Meta


• Nathan Matsuda, Meta


• Ajit Ninan, Meta


• Alexandre Chapiro^†, Meta


• Qi Sun^†, New York University

We studied six power-saving display mapping algorithms. These techniques are used in traditional display power reduction as well as recently-proposed methods for VR displays. Just objectionable difference (JOD) scores provide a unified measure of the magnitude of perceptual impact, and percentage values represent relative display power savings (OLED display shown here). Optimal techniques have low perceptual impact (perceived close to reference in JODs), but provide big power savings.

Abstract

---

Display power consumption is an emerging concern for untethered devices. This goes double for augmented and virtual extended reality (XR) displays, which target high refresh rates and high resolutions while conforming to an ergonomically light form factor. A number of image mapping techniques have been proposed to extend battery usage. However, there is currently no comprehensive quantitative understanding of how the power savings provided by these methods compare to their impact on visual quality. We set out to answer this question.

To this end, we present a perceptual evaluation of algorithms (PEA) for power optimization in XR displays (PODs). Consolidating a portfolio of six power-saving display mapping approaches, we begin by performing a large-scale perceptual study to understand the impact of each method on perceived quality in the wild. This results in a unified quality score for each technique, scaled in just-objectionable-difference (JOD) units. In parallel, each technique is analyzed using hardware-accurate power models.

The resulting JOD-to-Milliwatt transfer function provides a first-of-its-kind look into tradeoffs offered by display mapping techniques, and can be directly employed to make architectural decisions for power budgets on XR displays. Finally, we leverage our study data and power models to address important display power applications like the choice of display primary, power implications of eye tracking, and more.

 @article{ 
 chen2024peapods,
 author = {Chen, Kenneth and Wan, Thomas and Matsuda, Nathan and Ninan, Ajit and Chapiro, Alexandre and Sun, Qi},
 title = {PEA-PODs: Perceptual Evaluation of Algorithms for Power Optimization in XR Displays},
 year = {2024},
 issue_date = {July 2024},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 volume = {43},
 number = {4},
 issn = {0730-0301},
 url = {https://doi.org/10.1145/3658126},
 doi = {10.1145/3658126},
 journal = {ACM Trans. Graph.},
 month = {jul},
 articleno = {67},
 numpages = {17},
 } 

Links

---